pytorch实现mnister手写数字识别代码
时间: 2023-11-12 17:04:30 浏览: 69
很高兴回答您的问题。下面是基于PyTorch实现的MNIST手写数字识别代码示例:
首先,我们需要导入所需的 Python 库:
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
```
接着,我们可以定义一些超参数:
```python
batch_size = 64
learning_rate = 0.01
epochs = 10
```
然后,我们可以对数据进行预处理:
```python
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])
train_dataset = datasets.MNIST(
root='./data', train=True, download=True, transform=transform)
test_dataset = datasets.MNIST(
root='./data', train=False, download=True, transform=transform)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=True)
```
接下来,我们可以构建模型:
```python
class Net(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(1, 32, kernel_size=5)
self.conv2 = nn.Conv2d(32, 64, kernel_size=5)
self.fc1 = nn.Linear(1024, 128)
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
x = nn.functional.relu(nn.functional.max_pool2d(self.conv1(x), 2))
x = nn.functional.relu(nn.functional.max_pool2d(self.conv2(x), 2))
x = x.view(-1, 1024)
x = nn.functional.relu(self.fc1(x))
x = self.fc2(x)
return nn.functional.log_softmax(x, dim=1)
model = Net()
```
接着,我们可以定义损失函数和优化器:
```python
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=learning_rate, momentum=0.5)
```
最后,我们可以训练模型:
```python
for epoch in range(epochs):
for batch_idx, (data, target) in enumerate(train_loader):
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
if batch_idx % 250 == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.item()))
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
output = model(data)
test_loss += criterion(output, target).item()
pred = output.argmax(dim=1, keepdim=True)
correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))
```
希望这段代码可以帮助您实现基于 PyTorch 的手写数字识别模型。如果您有任何问题或疑问,请随时提出。
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